<ul data-eligibleForWebStory="true"><li>The paper focuses on enhancing data efficiency by curating web-crawl datasets through an advanced approach named EcoDatum.</li><li>EcoDatum addresses challenges related to unstructured and heterogeneous datasets, overcoming biases and the exclusion of relevant data often seen in traditional curation methods.</li><li>The method incorporates quality-guided deduplication for balanced feature distributions and integrates various data curation operators within a weak supervision ensemble framework.</li><li>Automated optimization is used to effectively score each data point, leading to improved curation quality and efficiency compared to existing techniques.</li><li>EcoDatum outperforms state-of-the-art methods and ranked 1st on the DataComp leaderboard, achieving an average performance score of 0.182 across 38 evaluation datasets.</li><li>The approach demonstrated a 28% improvement over the DataComp baseline method, showcasing its effectiveness in enhancing dataset curation and model training efficiency.</li></ul>

Quality over Quantity: Boosting Data Efficiency Through Ensembled Multimodal Data Curation

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